Damage prognosis: the future of structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 623-632 ◽  
Author(s):  
Charles R Farrar ◽  
Nick A.J Lieven
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Predictive Modelling ◽  
Economic Benefits ◽  
Remaining Useful Life ◽  
Grand Challenge ◽  
Damage State ◽  
Structural Health ◽  
Damage Prognosis ◽  
The Future

This paper concludes the theme issue on structural health monitoring (SHM) by discussing the concept of damage prognosis (DP). DP attempts to forecast system performance by assessing the current damage state of the system (i.e. SHM), estimating the future loading environments for that system, and predicting through simulation and past experience the remaining useful life of the system. The successful development of a DP capability will require the further development and integration of many technology areas including both measurement/processing/telemetry hardware and a variety of deterministic and probabilistic predictive modelling capabilities, as well as the ability to quantify the uncertainty in these predictions. The multidisciplinary and challenging nature of the DP problem, its current embryonic state of development, and its tremendous potential for life-safety and economic benefits qualify DP as a ‘grand challenge’ problem for engineers in the twenty-first century.

scholarly journals Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

scholarly journals Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 545 ◽  
Author(s):  
Xinlin Qing ◽  
Wenzhuo Li ◽  
Yishou Wang ◽  
Hu Sun
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Transducer ◽  
Piezoelectric Materials ◽  
Development Trend ◽  
Practical Implementation ◽  
Damage State ◽  
Structural Health ◽  
Safety And Reliability ◽  
Aircraft Application

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

Study on New Structural Health Monitoring Sensor of Aircraft

2014 ◽  
Vol 530-531 ◽  
pp. 62-65
Author(s):  
Yu Long Zhang ◽  
Wei Fang Zhang ◽  
Ai Ai Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Preparation Method ◽  
Core Component ◽  
Damage State ◽  
Film Sensor ◽  
Health Monitoring System ◽  
Structural Health ◽  
Damage Monitoring

Sensor is the core component of structural health monitoring system, which can collect the data of structural damage. The structural damage state can be gained after further processing. Aircraft serves in rigorous environment, and existing sensors cant meet the demand of its structural damage monitoring for inherent defect. A preparation method of partial poling piezoelectric film sensor was proposed in the paper, which can be used for structural damage monitoring of aircraft in combination with lamb wave.

scholarly journals Capacitance measurements on integrated conductors for detection of matrix cracks in GFRP

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Christina Buggisch ◽  
Abedin Gagani ◽  
Bodo Fiedler
Keyword(s):  
Structural Health Monitoring ◽  
Carbon Fibre ◽  
Health Monitoring ◽  
Glass Fibre ◽  
Crack Formation ◽  
Damage State ◽  
Monitoring Method ◽  
Capacitance Measurements ◽  
Structural Health

AbstractFor the reliable and cost-efficient application of glass fibre polymer composites in structural applications, knowledge of the damage state of the material during operation is necessary. Within this work, a structural health monitoring method based on in-situ electrical capacitance measurements is presented, which enables damage monitoring in glass fibre reinforced polymers. For this purpose, individual glass fibre rovings in a non-crimp fabric were replaced by carbon fibre rovings at regular intervals. Additionally, specimens with solid or stranded copper conductors were manufactured to gain insights into the influences of conductor material and composition. The modified fabrics were implemented as 90∘ layers of [0/904]s glass fibre polymer cross-ply laminates. To monitor the progressive damage, conductive rovings were contacted, forming the capacitor walls of interleaved capacitors. Carbon fibre conductors show higher sensitivity of the capacitance to crack formation than solid or stranded copper conductors. Capacitance decrease measured in-situ during tensile tests on specimens with carbon fibre conductors shows a high correlation with crack initiation, further crack formation and speed of crack evolution. An analytical model can describe the correlation based on the assumptions of an ideal plate capacitor. Thus, the structural health monitoring method developed in this work can reveal in-situ knowledge of the material damage state.

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